Publication Details

To Revise or Not to Revise

Learning to Detect Improvable Claims for Argumentative Writing Support

authored by
Gabriella Skitalinskaya, Henning Wachsmuth
Abstract

Optimizing the phrasing of argumentative text is crucial in higher education and professional development. However, assessing whether and how the different claims in a text should be revised is a hard task, especially for novice writers. In this work, we explore the main challenges to identifying argumentative claims in need of specific revisions. By learning from collaborative editing behaviors in online debates, we seek to capture implicit revision patterns in order to develop approaches aimed at guiding writers in how to further improve their arguments. We systematically compare the ability of common word embedding models to capture the differences between different versions of the same text, and we analyze their impact on various types of writing issues. To deal with the noisy nature of revision-based corpora, we propose a new sampling strategy based on revision distance. Opposed to approaches from prior work, such sampling can be done without employing additional annotations and judgments. Moreover, we provide evidence that using contextual information and domain knowledge can further improve prediction results. How useful a certain type of context is, depends on the issue the claim is suffering from, though.

Organisation(s)
Natural Language Processing Section
External Organisation(s)
University of Bremen
Type
Conference contribution
Pages
15799–15816
No. of pages
17
Publication date
07.2023
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Artificial Intelligence, Software, Computational Theory and Mathematics, Linguistics and Language
Electronic version(s)
https://arxiv.org/abs/2305.16799 (Access: Open)
https://doi.org/10.18653/v1/2023.acl-long.880 (Access: Open)